Enterprise Data Modeler

Job Title - Enterprise Data Modeler

Location: Richmond, VA - 23219 (Hybrid)

Tenure: 6+ Months

Job Description: Enterprise Data Modeler

The Information Technology Division is seeking a senior Data modeler to develop Data models for Data Assets and implementation of a cloud-based data management platform that will support the agency.

Enterprise data modeler provides expert support across the enterprise information framework, analyze and translate business needs into long-term solution data models by evaluating existing systems and working with a business and data architect to create conceptual data models , data flows . Develop best practices for Data Asset development, ensure consistency within the system and review modifications of existing cross-compatibility systems. Optimize data systems and evaluate implemented systems for variance discrepancies and efficiency. Maintain logical and physical data models along with accurate metadata.

Responsibilities:

Must have 10 years of experience with below skills
• Create conceptual data model to identify key business entities and visualize their relationships, define concepts and rules.
• Translate business needs into data models Build logical and physical data models for client hierarchy Document data designs for team.
• Present and communicate modeling results and recommendations to internal stakeholders and Development teams and explains features that may affect th
• Develop canonical models, Data as a service models and Knowledge of SOA to support integrations.
• Perform data profiling/analysis activities that helps to establish, modify and maintain data model
• Analyze data-related system integration challenges and propose appropriate solutions with strategic approach.
• Perform data profiling and analysis for maintaining data models Develop and support the usage of MDM toolkit Integrate source systems into the MDM sol
• Implement business rules for data reconciliation and deduplication Enforce data models and naming standards across deliverables.
• Establish processes for governing the identification, collection, and use of corporate metadata; take steps to assure metadata accuracy and validity
• Establish methods and procedures for tracking data quality, completeness, data redundancy, and improvement.
• Conduct data capacity planning, life cycle, duration, usage requirements, feasibility studies, and other tasks.